{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/matleap-a-fast-adaptive-matlab-ready-tau","title":"matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inference","arxiv_id":"1608.07058","date":"2016-08-25","proceeding":null,"authors":[],"abstract":"Background: Species abundance distributions in chemical reaction network\nmodels cannot usually be computed analytically. Instead, stochas- tic\nsimulation algorithms allow sample from the the system configuration. Although\nmany algorithms have been described, no fast implementation has been provided\nfor {\\tau}-leaping which i) is Matlab-compatible, ii) adap- tively alternates\nbetween SSA, implicit and explicit {\\tau}-leaping, and iii) provides summary\nstatistics necessary for Bayesian inference. Results: We provide a\nMatlab-compatible implementation of the adap- tive explicit-implicit\n{\\tau}-leaping algorithm to address the above-mentioned deficits. matLeap\nprovides equal or substantially faster results compared to two widely used\nsimulation packages while maintaining accuracy. Lastly, matLeap yields summary\nstatistics of the stochastic process unavailable with other methods, which are\nindispensable for Bayesian inference. Conclusions: matLeap addresses\nshortcomings in existing Matlab-compatible stochastic simulation software,\nproviding significant speedups and sum- mary statistics that are especially\nuseful for researchers utilizing particle- filter based methods for Bayesian\ninference. Code is available for download at\nhttps://github.com/claassengroup/matLeap. Contact:\njustin.feigelman@imsb.biol.ethz.ch","url_abs":"http://arxiv.org/abs/1608.07058v1","url_pdf":"http://arxiv.org/pdf/1608.07058v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"matleap-a-fast-adaptive-matlab-ready-tau","repo_url":"https://github.com/claassengroup/matLeap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}